Application of recurrent neural networks for motion analysis with OpenSim

Application of recurrent neural networks for motion analysis with OpenSim

This paper explores the application of Recurrent Neural Networks (RNN) – Long Short-Term Memory (LSTM) networks in particular, to predict muscle force and movement during stoop lift motions with data processed using OpenSim software. The study includes machine learning techniques such as biomechanic...

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Bibliographic Details
Main Author: Yam, Li Hao
Other Authors: Yifan Wang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Engineering
Physics
OpenSim
Gait
Motion analysis
Recurrent neural networks
Machine learning
Data science
Long short-term memory networks
Online Access:https://hdl.handle.net/10356/177883
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Institution: Nanyang Technological University
Language: English
Description
Summary:This paper explores the application of Recurrent Neural Networks (RNN) – Long Short-Term Memory (LSTM) networks in particular, to predict muscle force and movement during stoop lift motions with data processed using OpenSim software. The study includes machine learning techniques such as biomechanical data analysis, feature selection via Random Forest and hyperparameter optimization with Optuna study to improve the accuracy of the LSTM model. The created LSTM model is able to process complex sequential biomechanical data which has the potential to positively impact stroke patients’ lower limb rehabilitation. By providing more precise and individualized treatment insights, machine learning approaches can prospectively revolutionize rehabilitation practices in the 21st century.

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